This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Aurora is glass-box quantitative AI that runs locally. Drop in a dataset and 24+ research-grade methods do the real math — anomalies, causality, regimes, forecasts — then every finding is cited to the method and source behind it. A live "0 fabricated" chip guarantees no invented numbers. Cloud LLMs guess; Aurora computes. Includes an MCP server + Python SDK so AI agents can call it for verified math. 100% local, no cloud, no API keys. Open source, Apache 2.0.
Hey Product Hunt!
I built Aurora out of a frustration I couldn't shake: I'd hand an LLM a dataset, get back confident numbers and conclusions, and have no way to tell which were real and which were invented. For work that actually matters, that's disqualifying — and bigger models don't fix it.
So Aurora flips the architecture. Classical statistical methods (change-point detection, HMM regimes, anomaly consensus, Granger causality, forecasting — 17+ of them) do the actual computation. A local LLM only narrates the results, and it can only say things it can cite back to the method that produced them. Every finding carries its method, severity, and source. The "0 fabricated" counter isn't marketing — it's audited, and it's the whole point.
It runs 100% on your machine — no cloud, no API keys, your data never leaves. There's a studio for humans, plus an MCP server and Python SDK so AI agents (Claude Desktop, Cursor, custom) can call it when they need real math instead of guesses.
It's open source (Apache 2.0) and honestly still has rough edges — I ship the changelog in public, including what doesn't work yet.
What I'd genuinely love feedback on: does the computation-vs-narration split hold up under scrutiny? And what would it take for you to trust an AI tool with data that actually matters?
Thanks for looking — I'll be here all day answering everything.
How does the "0 fabricated" chip actually work under the hood, like is it catching hallucinations at the method level or just flagging outputs that lack source citations?
How does Aurora actually guarantee no fabricated numbers when it pulls from local datasets, and does that guarantee still hold if the data itself is incomplete or messy?
Ran the changepoint and causal inference tools on a messy sales dataset and the citations to the underlying method made it really easy to sanity check the output. Love that the 0 fabricated chip is front and center instead of buried somewhere.
Finally tried Aurora on a messy returns dataset and the regime detection flagged a transition I'd been manually squinting at for weeks. Citations to the actual method make it feel honest in a way most quant tools don't.
About Aurora on Product Hunt
“Glass-box Quantitative AI for Humans and Agents”
Aurora was submitted on Product Hunt and earned 5 upvotes and 9 comments, placing #92 on the daily leaderboard. Aurora is glass-box quantitative AI that runs locally. Drop in a dataset and 24+ research-grade methods do the real math — anomalies, causality, regimes, forecasts — then every finding is cited to the method and source behind it. A live "0 fabricated" chip guarantees no invented numbers. Cloud LLMs guess; Aurora computes. Includes an MCP server + Python SDK so AI agents can call it for verified math. 100% local, no cloud, no API keys. Open source, Apache 2.0.
Aurora was featured in Artificial Intelligence (473.7k followers), GitHub (41.3k followers), Data & Analytics (5.7k followers) and Data Science (3.9k followers) on Product Hunt. Together, these topics include over 136.2k products, making this a competitive space to launch in.
Who hunted Aurora?
Aurora was hunted by Brandon Grutkowski. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Aurora stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Brandon Grutkowski